Merge pull request #514 from scikit-learn-contrib/511-modulenotfounderror-no-module-named-mapieconformity_scoresresidual_conformity_scores

vincentblot28 · web-flow · commit c23039a5f7e1 · 2024-09-12T17:59:52.000+02:00
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
@@ -0,0 +1,165 @@
+from abc import ABCMeta, abstractmethod
+
+from sklearn.utils import deprecated
+
+from mapie.conformity_scores.regression import BaseConformityScore
+from mapie._machine_precision import EPSILON
+from mapie._typing import NDArray
+
+
+@deprecated(
+    "WARNING: Deprecated path to import ConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores import BaseRegressionScore]."
+)
+class ConformityScore(BaseConformityScore, metaclass=ABCMeta):
+    """
+    Base conformity score class for regression task.
+
+    This class should not be used directly. Use derived classes instead.
+
+    Parameters
+    ----------
+    sym: bool
+        Whether to consider the conformity score as symmetrical or not.
+
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+    """
+
+    def __init__(
+        self,
+        sym: bool,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__()
+        self.sym = sym
+        self.consistency_check = consistency_check
+        self.eps = eps
+
+    @abstractmethod
+    def get_signed_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_conformity_scores``.
+        Subclasses should implement this method!
+
+        Compute the sample conformity scores given the predicted and
+        observed targets.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Signed conformity scores.
+        """
+
+    @abstractmethod
+    def get_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_conformity_scores``.
+        Subclasses should implement this method!
+
+        Compute the sample conformity scores given the predicted and
+        observed targets.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+
+    @abstractmethod
+    def get_estimation_distribution(
+        self,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Observed values.
+        """
+
+    @abstractmethod
+    def predict_set(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        **kwargs
+    ):
+        """
+        Compute the prediction sets on new samples based on the uncertainty of
+        the target confidence set.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples,)
+            The input data or samples for prediction.
+
+        alpha_np: NDArray of shape (n_alpha, )
+            Represents the uncertainty of the confidence set to produce.
+
+        **kwargs: dict
+            Additional keyword arguments.
+
+        Returns:
+        --------
+        The output structure depend on the subclass.
+            The prediction sets for each sample and each alpha level.
+        """
diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/residual_conformity_scores.py
@@ -0,0 +1,34 @@
+from sklearn.utils import deprecated
+
+from .bounds import (
+    AbsoluteConformityScore as NewAbsoluteConformityScore,
+    GammaConformityScore as NewGammaConformityScore,
+    ResidualNormalisedScore as NewResidualNormalisedScore
+)
+
+
+@deprecated(
+    "WARNING: Deprecated path to import AbsoluteConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import AbsoluteConformityScore]."
+)
+class AbsoluteConformityScore(NewAbsoluteConformityScore):
+    pass
+
+
+@deprecated(
+    "WARNING: Deprecated path to import GammaConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import GammaConformityScore]."
+)
+class GammaConformityScore(NewGammaConformityScore):
+    pass
+
+
+@deprecated(
+    "WARNING: Deprecated path to import ResidualNormalisedScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import ResidualNormalisedScore]."
+)
+class ResidualNormalisedScore(NewResidualNormalisedScore):
+    pass
diff --git a/mapie/conformity_scores/utils_classification_conformity_scores.py b/mapie/conformity_scores/utils_classification_conformity_scores.py
@@ -0,0 +1,14 @@
+from sklearn.utils import deprecated
+
+from mapie.conformity_scores.sets.utils import (
+    get_true_label_position as get_true_label_position_new_path,
+)
+
+
+@deprecated(
+    "WARNING: Deprecated path to import get_true_label_position. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.sets.utils import get_true_label_position]."
+)
+def get_true_label_position(*args, **kwargs):
+    return get_true_label_position_new_path(*args, **kwargs)
diff --git a/mapie/estimator/estimator.py b/mapie/estimator/estimator.py
@@ -0,0 +1,12 @@
+from sklearn.utils import deprecated
+
+from mapie.estimator.regressor import EnsembleRegressor as NewEnsembleRegressor
+
+
+@deprecated(
+    "WARNING: Deprecated path to import EnsembleRegressor. "
+    "Please prefer the new path: "
+    "[from mapie.estimator.regressor import EnsembleRegressor]."
+)
+class EnsembleRegressor(NewEnsembleRegressor):
+    pass
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
@@ -11,13 +11,14 @@
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 
+from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
 from mapie.regression import MapieQuantileRegressor, MapieRegressor
 
 X_toy = np.arange(18).reshape(-1, 1)
 y_toy = np.array(
     [0, 0, 1, 0, 1, 2, 1, 2, 2, 0, 0, 1, 0, 1, 2, 1, 2, 2]
-    )
+)
 
 
 def MapieSimpleEstimators() -> List[BaseEstimator]:
@@ -195,3 +196,121 @@ def test_sklearn_compatible_estimator(
 ) -> None:
     """Check compatibility with sklearn, using sklearn estimator checks API."""
     check(estimator)
+
+
+def test_warning_when_import_from_gamma_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            GammaConformityScore
+        )
+        GammaConformityScore()
+
+
+def test_warning_when_import_from_absolute_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            AbsoluteConformityScore
+        )
+        AbsoluteConformityScore()
+
+
+def test_warning_when_import_from_residual_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            ResidualNormalisedScore
+        )
+        ResidualNormalisedScore()
+
+
+def test_warning_when_import_from_conformity_scores():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.conformity_score"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.conformity_scores import (
+            ConformityScore
+        )
+
+        class DummyConformityScore(ConformityScore):
+            def __init__(self) -> None:
+                super().__init__(sym=True, consistency_check=True)
+
+            def get_signed_conformity_scores(
+                self, y: ArrayLike, y_pred: ArrayLike, **kwargs
+            ) -> NDArray:
+                return np.array([])
+
+            def get_estimation_distribution(
+                self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
+            ) -> NDArray:
+                """
+                A positive constant is added to the sum between predictions and
+                conformity scores to make the estimated distribution
+                inconsistent with the conformity score.
+                """
+                return np.array([])
+
+            def get_conformity_scores(
+                self, y: ArrayLike, y_pred: ArrayLike, **kwargs
+            ) -> NDArray:
+                return np.array([])
+
+            def predict_set(
+                self, X: NDArray, alpha_np: NDArray, **kwargs
+            ) -> NDArray:
+                return np.array([])
+
+        dcs = DummyConformityScore()
+        dcs.get_signed_conformity_scores(y_toy, y_toy)
+        dcs.get_estimation_distribution(y_toy, y_toy)
+        dcs.get_conformity_scores(y_toy, y_toy)
+        dcs.predict_set(y_toy, 0.5)
+
+
+def test_warning_when_import_from_old_get_true_label_position():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.utils_classification_conformity_scores\
+              import get_true_label_position
+        get_true_label_position(np.array([[0.1, 0.2, 0.7]]), np.array([2]))
+
+
+def test_warning_when_import_from_estimator():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.estimator.estimator"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.estimator.estimator import EnsembleRegressor
+        EnsembleRegressor(
+            estimator=LinearRegression(),
+            method="naive",
+            cv=3,
+            agg_function="mean",
+            n_jobs=1,
+            random_state=0,
+            test_size=0.2,
+            verbose=0,
+        )
